Research Spotlight: Samuel Jenness

July 21, 2020

Samuel Jenness, PhD, assistant professor in the Department of Epidemiology, was still a doctoral student completing his dissertation at the University of Washington when he and members of the faculty developed EpiModel, a software platform in the R computing platform that specializes in representing dynamic human contact networks, which serve as the pathway through which infectious diseases are transmitted across a population.

In the years since its conception, Jenness has used EpiModel in the classroom setting, where his MPH and PHD students can build and run their own models in class. And, with an NIH grant supporting it, Jenness continues to hone and extend the software package with the end goal of serving the greater global community of infectious disease modelers. While most of Jenness’s previous usage has related to his work in HIV and other STIs—which was his motivation for developing it initially—he notes its flexibility and applicability to a wide range of users, including those modeling COVID-19.

In the past few months, EpiModel has seen a significant surge in downloads, averaging 3,500 downloads a month over the last few months. As of mid-May, the free software had been downloaded around 120,000 times since its origins. 

“It’s an exciting time to be an infectious disease modeler because of the level of interest from the wider public and exposure of what modeling is and what it does,” says Jenness. “It feels like we’re doing something that’s really useful.”

Jenness’s contributions to the COVID-19 pandemic don’t stop with EpiModel. He’s used his modeling expertise to respond in a variety of additional ways. For instance, he’s currently engaged in a collaboration with researchers at Yale University, including Saad Omer, to develop a network model of COVID-19 on ships (cruise ships and military ships), with the intent of discovering different interventions and design changes that might be possible in those unique environments.

For the model, the researchers are using data tied to the outbreak on the Diamond Princess cruise ship and abstracting from that to develop a network framework to understand the problem in the absence of a long-term biomedical solution (like a vaccine). The researchers are looking at the preliminary data and asking themselves questions like: What is optimal way these ships can be designed moving forward to prevent new clusters of disease? When is the ideal time for a network lockdown to occur relative to the first diagnosis within that environment?

Among the scenarios they are exploring is the sectarization of ships. So, instead of having one big container where everyone is together (the most ideal way for pathogens to spread), the ship could be split into sectors with crew and passengers self-contained within sections.

“Our goal is to estimate how how rapidly social distancing within a ship will have to change,” says Jenness. “We hope that the science can inform guidelines for the cruise ship industry moving forward.”

Jenness and Omer are also engaged in a separate five-year NIH-funded project together, along with Ben Lopman, called GlobalMix. That study aims to characterize contact patterns for infectious diseases in low- and middle-income countries—specifically Mozambique, Guatemala, India, and Pakistan—and to then parameterize models of infectious disease within those settings. Since the project received funding, COVID-19 of course has taken hold, which means the data collection methods and the meaning of the data have evolved from informing a generic respiratory disease pandemic to a very real one. But, as infectious disease modelers, adaptability is all part of the territory.

The same can be said of a parallel study being conducted by the group called, CorporateMix. It’s a similar idea, but takes place in domestic workplace settings. In this study, the researchers recruited three companies to administer a survey to their employees and get the contact pattern of their employees. While the original intent was for data to represent normal contact patterns within a business, the results will now be impacted by COVID-19, with much of the data collection occurring during periods when employees were teleworking.

“We had intended for that type of data to represent normal contact patterns, but now we’re getting data where everyone’s on lockdown,” says Jenness.

Meanwhile, on campus, Jenness and Lopman are both actively involved with Emory University’s planning efforts as it prepares to reopen in the fall, with Jenness assisting in epidemic modeling.

“I’m motivated to work toward finding solutions,” says Jenness. “Given that we’re often presented with two extremes where we do nothing or we completely shut society down—we’ve already found that both are untenable for a variety of reasons—the interesting scientific question is, where do we find that middle ground? What steps can we take to prevent a significant amount of morbidity and mortality at the population level, in particular subsets of the population most vulnerable to severe infection or to rapidly growing outbreaks?”